Information processing and decision support can be accomplished using a learning system. Examples of such systems include U.S. Pat. No. 6,115,702, entitled, “Automatic Determination of Report Granularity” (hereinafter referred to as the '702 patent) and U.S. Pat. No. 6,125,339, entitled, “Automatic Learning of Belief Functions” (hereinafter referred to as the '339 patent).
The '702 patent describes a method for performing statistical classification that resolves conflict in independent sources of information. Such a system resolves the conflict by gathering sets of information representative of features of an object or event; creating basic probability assignments based on the sets of information; determining a coarse information set from the sets of information; performing coarsening on the sets of information; performing linear superposition on each feature; and combining all features to reach a conclusion.
The '339 patent describes automatic learning belief functions that enable the combination of different, and possibly contradictory information sources. The '339 patent uses such functions to determine erroneous information sources, inappropriate information combinations, and optimal information granularities, along with enhanced system performance.
While operable for basic learning systems, the techniques described above only apply to cases where the information sources relate to a single system variable for which a certain decision must be made based on the inputs.
Another prior art reference, entitled, “The evaluation of Sensors' Reliability and their tuning for multisensor data fusion within the transferable belief model,” describes a method for tuning sensor input discounting weights in a multi-sensor fusion system. The reference was authored by Zied Elouedi, Khaled Mellouli, and Philippe Smets (Zied et al.), and published in Proceedings of 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-2001), September 2001, Toulouse, France. pp. 350-361. As was the case with the patents listed above, the approach described by Zied et al. only applies to a single variable system (that is, both the multi-sensor inputs and the outputs of the fusion are on the same variable). Further, Zied et al. does not provide any road map or other references for extending the approach to a multi-variable general fusion system.
Another reference, entitled, “Some Strategies for Explanations in Evidential Reasoning,” contains formulations for sensitivity analysis. This reference was written by Hong Xu and Philippe Smets, and published in IEEE Trans. on SMC-A Vol. 26, No. 5, pp. 599-607. September 1996. While the authors described formulations for sensitivity analysis, they did not use the formulation for learning.
Thus, a continuing need exists for a learning system that is operable for general reasoning problems utilizing a sensitivity analysis formulation.